{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T09:01:57Z","timestamp":1777712517427,"version":"3.51.4"},"update-to":[{"DOI":"10.1371\/journal.pcbi.1006196","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2018,6,8]],"date-time":"2018-06-08T00:00:00Z","timestamp":1528416000000}}],"reference-count":41,"publisher":"Public Library of Science (PLoS)","issue":"5","license":[{"start":{"date-parts":[[2018,5,29]],"date-time":"2018-05-29T00:00:00Z","timestamp":1527552000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004359","name":"Vetenskapsr\u00e5det","doi-asserted-by":"publisher","award":["2016-03352"],"award-info":[{"award-number":["2016-03352"]}],"id":[{"id":"10.13039\/501100004359","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004359","name":"Vetenskapsr\u00e5det","doi-asserted-by":"publisher","award":["013-61X-08276-26-4"],"award-info":[{"award-number":["013-61X-08276-26-4"]}],"id":[{"id":"10.13039\/501100004359","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Swedish e-Science Research Center"},{"name":"Knut och Alice Wallenbergs Stiftelse (SE)","award":["2016.0473"],"award-info":[{"award-number":["2016.0473"]}]}],"content-domain":{"domain":["www.ploscompbiol.org"],"crossmark-restriction":false},"short-container-title":["PLoS Comput Biol"],"DOI":"10.1371\/journal.pcbi.1006196","type":"journal-article","created":{"date-parts":[[2018,5,29]],"date-time":"2018-05-29T13:41:53Z","timestamp":1527601313000},"page":"e1006196","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":34,"title":["Simulations to benchmark time-varying connectivity methods for fMRI"],"prefix":"10.1371","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0533-6035","authenticated-orcid":true,"given":"William Hedley","family":"Thompson","sequence":"first","affiliation":[]},{"given":"Craig Geoffrey","family":"Richter","sequence":"additional","affiliation":[]},{"given":"Pontus","family":"Plav\u00e9n-Sigray","sequence":"additional","affiliation":[]},{"given":"Peter","family":"Fransson","sequence":"additional","affiliation":[]}],"member":"340","published-online":{"date-parts":[[2018,5,29]]},"reference":[{"key":"ref1","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1016\/j.neuroimage.2015.03.062","article-title":"Electrophysiological signatures of spontaneous BOLD fluctuations in macaque prefrontal cortex","volume":"113","author":"RM Hutchison","year":"2015","journal-title":"NeuroImage. Elsevier Inc"},{"key":"ref2","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1016\/j.neuroimage.2013.12.063","article-title":"Dynamic changes of spatial functional network connectivity in healthy individuals and schizophrenia patients using independent vector analysis","volume":"90","author":"S Ma","year":"2014","journal-title":"NeuroImage"},{"key":"ref3","first-page":"1","article-title":"Dynamic Resting-State Functional Connectivity in Major Depression","author":"RH Kaiser","year":"2015","journal-title":"Neuropsychopharmacology. Nature Publishing Group"},{"key":"ref4","doi-asserted-by":"crossref","first-page":"520","DOI":"10.1038\/s41598-017-00425-z","article-title":"Positive affect, surprise, and fatigue are correlates of network flexibility","volume":"7","author":"RF Betzel","year":"2017","journal-title":"Scientific Reports. Springer US"},{"key":"ref5","first-page":"bhw029","article-title":"Dynamic Brain Network Correlates of Spontaneous Fluctuations in Attention","author":"A Kucyi","year":"2016","journal-title":"Cerebral cortex (New York, NY: 1991)"},{"key":"ref6","doi-asserted-by":"crossref","first-page":"E5219","DOI":"10.1073\/pnas.1418031112","article-title":"Signature of consciousness in the dynamics of resting-state brain activity","volume":"112","author":"P Barttfeld","year":"2015","journal-title":"Proceedings of the National Academy of Sciences"},{"key":"ref7","doi-asserted-by":"crossref","first-page":"735","DOI":"10.1089\/brain.2016.0454","article-title":"On Stabilizing the Variance of Dynamic Functional Brain Connectivity Time Series","volume":"6","author":"WH Thompson","year":"2016","journal-title":"Brain Connectivity"},{"key":"ref8","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1162\/NETN_a_00011","article-title":"From static to temporal network theory\u2014applications to functional brain connectivity","volume":"1","author":"WH Thompson","year":"2017","journal-title":"Network Neruoscience"},{"key":"ref9","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1016\/j.neuroimage.2015.12.001","article-title":"Dynamic fluctuations coincide with periods of high and low modularity in resting-state functional brain networks","volume":"127","author":"RF Betzel","year":"2016","journal-title":"NeuroImage"},{"key":"ref10","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3389\/fnhum.2015.00398","article-title":"The mean\u2013variance relationship reveals two possible strategies for dynamic brain connectivity analysis in fMRI","volume":"9","author":"WH Thompson","year":"2015","journal-title":"Frontiers in Human Neuroscience"},{"key":"ref11","doi-asserted-by":"crossref","first-page":"39156","DOI":"10.1038\/srep39156","article-title":"Bursty properties revealed in large-scale brain networks with a point-based method for dynamic functional connectivity","volume":"6","author":"WH Thompson","year":"2016","journal-title":"Scientific Reports. Nature Publishing Group"},{"key":"ref12","first-page":"1","article-title":"On the Stability of BOLD fMRI Correlations","author":"TO Laumann","year":"2016","journal-title":"Cerebral Cortex"},{"key":"ref13","doi-asserted-by":"crossref","first-page":"466","DOI":"10.1016\/j.neuroimage.2015.03.047","article-title":"Towards a statistical test for functional connectivity dynamics","volume":"114","author":"A Zalesky","year":"2015","journal-title":"NeuroImage. Elsevier Inc"},{"key":"ref14","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1016\/j.neuroimage.2015.11.055","article-title":"Can sliding-window correlations reveal dynamic functional connectivity in resting-state fMRI?","volume":"127","author":"R Hindriks","year":"2016","journal-title":"NeuroImage. The Authors"},{"key":"ref15","doi-asserted-by":"crossref","first-page":"663","DOI":"10.1093\/cercor\/bhs352","article-title":"Tracking whole-brain connectivity dynamics in the resting state","volume":"24","author":"EA Allen","year":"2014","journal-title":"Cerebral Cortex"},{"key":"ref16","doi-asserted-by":"crossref","first-page":"399","DOI":"10.1016\/j.neuroimage.2015.07.064","article-title":"Estimation of dynamic functional connectivity using Multiplication of Temporal Derivatives","volume":"122","author":"JM Shine","year":"2015","journal-title":"NeuroImage. Elsevier Inc"},{"key":"ref17","doi-asserted-by":"crossref","first-page":"4392","DOI":"10.1073\/pnas.1216856110","article-title":"Time-varying functional network information extracted from brief instances of spontaneous brain activity","volume":"110","author":"X Liu","year":"2013","journal-title":"Proceedings of the National Academy of Sciences of the United States of America"},{"key":"ref18","doi-asserted-by":"crossref","first-page":"937","DOI":"10.1016\/j.neuroimage.2013.07.019","article-title":"Principal components of functional connectivity: a new approach to study dynamic brain connectivity during rest","volume":"83","author":"N Leonardi","year":"2013","journal-title":"NeuroImage. Elsevier Inc"},{"key":"ref19","doi-asserted-by":"crossref","first-page":"15","DOI":"10.3389\/fphys.2012.00015","article-title":"Criticality in large-scale brain FMRI dynamics unveiled by a novel point process analysis","volume":"3","author":"E Tagliazucchi","year":"2012","journal-title":"Frontiers in physiology"},{"key":"ref20","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3389\/fnins.2016.00381","article-title":"The Voxel-Wise Functional Connectome Can Be Efficiently Derived from Co-activations in a Sparse Spatio-Temporal Point-Process","volume":"10","author":"E Tagliazucchi","year":"2016","journal-title":"Frontiers in Neuroscience"},{"key":"ref21","doi-asserted-by":"crossref","first-page":"1222","DOI":"10.1016\/j.neuroimage.2011.03.033","article-title":"Characterizing dynamic functional connectivity in the resting brain using variable parameter regression and Kalman filtering approaches","volume":"56","author":"J Kang","year":"2011","journal-title":"NeuroImage. Elsevier Inc"},{"key":"ref22","doi-asserted-by":"crossref","first-page":"791","DOI":"10.1016\/j.neuroimage.2015.10.088","article-title":"State space modeling of time-varying contemporaneous and lagged relations in connectivity maps","volume":"125","author":"PCM Molenaar","year":"2016","journal-title":"NeuroImage. Elsevier B.V"},{"key":"ref23","doi-asserted-by":"crossref","first-page":"780","DOI":"10.1089\/brain.2014.0253","article-title":"DynamicBC: A MATLAB Toolbox for Dynamic Brain Connectome Analysis","volume":"4","author":"W Liao","year":"2014","journal-title":"Brain Connectivity"},{"key":"ref24","doi-asserted-by":"crossref","first-page":"3131","DOI":"10.1073\/pnas.1121329109","article-title":"Temporally-independent functional modes of spontaneous brain activity","volume":"109","author":"SM Smith","year":"2012","journal-title":"Proceedings of the National Academy of Sciences of the United States of America"},{"key":"ref25","doi-asserted-by":"crossref","first-page":"339","DOI":"10.1089\/brain.2011.0036","article-title":"A sliding time-window ICA reveals spatial variability of the default mode network in time","volume":"1","author":"V Kiviniemi","year":"2011","journal-title":"Brain connectivity"},{"key":"ref26","doi-asserted-by":"crossref","first-page":"531","DOI":"10.1016\/j.neuroimage.2014.06.052","article-title":"Evaluating dynamic bivariate correlations in resting-state fMRI: A comparison study and a new approach","volume":"101","author":"MA Lindquist","year":"2014","journal-title":"NeuroImage. Elsevier Inc"},{"key":"ref27","doi-asserted-by":"crossref","first-page":"561","DOI":"10.1016\/j.neuroimage.2016.10.044","article-title":"Cortical rich club regions can organize state-dependent functional network formation by engaging in oscillatory behavior","volume":"146","author":"M Senden","year":"2017","journal-title":"NeuroImage. Elsevier"},{"key":"ref28","doi-asserted-by":"crossref","first-page":"666","DOI":"10.1007\/s10548-014-0406-2","article-title":"Characterizing and Differentiating Brain State Dynamics via Hidden Markov Models","volume":"28","author":"J Ou","year":"2015","journal-title":"Brain Topography"},{"key":"ref29","doi-asserted-by":"crossref","first-page":"e1005138","DOI":"10.1371\/journal.pcbi.1005138","article-title":"Temporal Dynamics and Developmental Maturation of Salience, Default and Central-Executive Network Interactions Revealed by Variational Bayes Hidden Markov Modeling","volume":"12","author":"S Ryali","year":"2016","journal-title":"PLOS Computational Biology"},{"key":"ref30","doi-asserted-by":"crossref","first-page":"e55","DOI":"10.7717\/peerj-cs.55","article-title":"Probabilistic programming in Python using PyMC3","volume":"2","author":"J Salvatier","year":"2016","journal-title":"PeerJ Computer Science"},{"key":"ref31","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1109\/MCSE.2011.37","article-title":"The NumPy array: A structure for efficient numerical computation","volume":"13","author":"S Van Der Walt","year":"2011","journal-title":"Computing in Science and Engineering"},{"key":"ref32","first-page":"10","article-title":"SciPy: Open source scientific tools for Python","volume":"9","author":"TE Oliphant","year":"2007","journal-title":"Computing in Science and Engineering"},{"key":"ref33","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1109\/MCSE.2007.55","article-title":"Matplotlib: A 2D graphics environment","volume":"9","author":"JD Hunter","year":"2007","journal-title":"Computing in Science and Engineering"},{"key":"ref34","unstructured":"Waskom M, Botvinnik O, Drewokane, Hobson P, David, Halchenko Y, et al. seaborn: v0.7.1 (June 2016). doiorg. Zenodo;"},{"key":"ref35","doi-asserted-by":"crossref","first-page":"430","DOI":"10.1016\/j.neuroimage.2014.09.007","article-title":"On spurious and real fluctuations of dynamic functional connectivity during rest","volume":"104","author":"N Leonardi","year":"2015","journal-title":"NeuroImage. Elsevier Inc"},{"key":"ref36","doi-asserted-by":"crossref","first-page":"896","DOI":"10.1016\/j.neuroimage.2017.12.057","article-title":"A common framework for the problem of deriving estimates of dynamic functional brain connectivity","volume":"172","author":"WH Thompson","year":"2018","journal-title":"NeuroImage. Elsevier Ltd"},{"key":"ref37","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.neuroimage.2015.04.040","article-title":"A jackknife approach to quantifying single-trial correlation between covariance-based metrics undefined on a single-trial basis","volume":"114","author":"CG Richter","year":"2015","journal-title":"NeuroImage. Elsevier B.V"},{"key":"ref38","first-page":"30","article-title":"The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo","volume":"15","author":"M Hoffman","year":"2014","journal-title":"Journal of Machine Learning Research"},{"key":"ref39","first-page":"867","article-title":"A Widely Applicable Bayesian Information Criterion","volume":"14","author":"S Watanabe","year":"2013","journal-title":"Journal of Machine Learning Research"},{"key":"ref40","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1002\/hbm.460020402","article-title":"Statistical parametric maps in functional imaging: A general linear approach","volume":"2","author":"KJ Friston","year":"1995","journal-title":"Human Brain Mapping"},{"key":"ref41","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/j.neuroimage.2016.02.074","article-title":"Evaluation of sliding window correlation performance for characterizing dynamic functional connectivity and brain states","volume":"133","author":"S Shakil","year":"2016","journal-title":"NeuroImage"}],"updated-by":[{"DOI":"10.1371\/journal.pcbi.1006196","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2018,6,8]],"date-time":"2018-06-08T00:00:00Z","timestamp":1528416000000}}],"container-title":["PLOS Computational Biology"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/dx.plos.org\/10.1371\/journal.pcbi.1006196","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,10,18]],"date-time":"2019-10-18T15:52:11Z","timestamp":1571413931000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pcbi.1006196"}},"subtitle":[],"editor":[{"given":"Daniele","family":"Marinazzo","sequence":"first","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2018,5,29]]},"references-count":41,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2018,5,29]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pcbi.1006196","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/212241","asserted-by":"object"}]},"ISSN":["1553-7358"],"issn-type":[{"value":"1553-7358","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,5,29]]}}}